Consciousness science: where are we, where are we going, and what if we get there?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the biophysical basis of consciousness remains a substantial challenge for 21st-century science. This endeavor is becoming even more pressing in light of accelerating progress in artificial intelligence and other technologies. In this article, we provide an overview of recent developments in the scientific study of consciousness and consider possible futures for the field. We highlight how several novel approaches may facilitate new breakthroughs, including increasing attention to theory development, adversarial collaborations, greater focus on the phenomenal character of conscious experiences, and the development and use of new methodologies and ecological experimental designs. Our emphasis is forward-looking: we explore what “success” in consciousness science may look like, with a focus on clinical, ethical, societal, and scientific implications. We conclude that progress in understanding consciousness will reshape how we see ourselves and our relationship to both artificial intelligence and the natural world, usher in new realms of intervention for modern medicine, and inform discussions around both nonhuman animal welfare and ethical concerns surrounding the beginning and end of human life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it